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A Sport Recognition Method with Utilizing Less Motion Sensors

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 329)

Abstract

In this study, we propose a recognition method in ball games using no more than two triaxial accelerometers on the user’s front arm and upper arm to track motion data. To produce effective features for classifying ball games’ postures, the motion data is processed by our method, which includes a median filter, a duplication removal algorithm, and an algorithm of feature extraction. Subsequently, the produced features are recognized by a support vector machine scheme for sports with single-handed swings like tennis, badminton, and ping pong. The research result in this investigation can help the athlete training of the above mentioned sports. Experimental results showed that the precision rate of the proposed method for recognizing postures in a single-handed swing achieves 95.67%.

Keywords

Accelerometry single-handed swing swing features feature extraction swinging posture recognition 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science & Information EngineeringNational Yunlin University of Science & TechnologyDouliuTaiwan
  2. 2.Department of Computer Science and Information EngineeringNational Penghu University of Science & TechnologyMakungTaiwan

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